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Ganiga R, S. N. M, Choi W, Pan S. ResNet1D-Based Personal Identification with Multi-Session Surface Electromyography for Electronic Health Record Integration. SENSORS (BASEL, SWITZERLAND) 2024; 24:3140. [PMID: 38793994 PMCID: PMC11124878 DOI: 10.3390/s24103140] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/27/2024] [Revised: 05/07/2024] [Accepted: 05/11/2024] [Indexed: 05/26/2024]
Abstract
Personal identification is an important aspect of managing electronic health records (EHRs), ensuring secure access to patient information, and maintaining patient privacy. Traditionally, biometric, signature, username/password, photo identity, etc., are employed for user authentication. However, these methods can be prone to security breaches, identity theft, and user inconvenience. The security of personal information is of paramount importance, particularly in the context of EHR. To address this, our study leverages ResNet1D, a deep learning architecture, to analyze surface electromyography (sEMG) signals for robust identification purposes. The proposed ResNet1D-based personal identification approach using the sEMG signal can offer an alternative and potentially more secure method for personal identification in EHR systems. We collected a multi-session sEMG signal database from individuals, focusing on hand gestures. The ResNet1D model was trained using this database to learn discriminative features for both gesture and personal identification tasks. For personal identification, the model validated an individual's identity by comparing captured features with their own stored templates in the healthcare EHR system, allowing secure access to sensitive medical information. Data were obtained in two channels when each of the 200 subjects performed 12 motions. There were three sessions, and each motion was repeated 10 times with time intervals of a day or longer between each session. Experiments were conducted on a dataset of 20 randomly sampled subjects out of 200 subjects in the database, achieving exceptional identification accuracy. The experiment was conducted separately for 5, 10, 15, and 20 subjects using the ResNet1D model of a deep neural network, achieving accuracy rates of 97%, 96%, 87%, and 82%, respectively. The proposed model can be integrated with healthcare EHR systems to enable secure and reliable personal identification and the safeguarding of patient information.
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Affiliation(s)
- Raghavendra Ganiga
- Department of Information and Communication Technology, Manipal Institute of Technology, Manipal Academy of Higher Education (MAHE), Manipal 576104, India;
| | - Muralikrishna S. N.
- Department of Computer Science and Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education (MAHE), Manipal 576104, India
| | - Wooyeol Choi
- Department of Computer Engineering, Chosun University, Gwangju 61452, Republic of Korea;
| | - Sungbum Pan
- IT Research Institute, Chosun University, 309 Pilmun-daero, Gwangju 61452, Republic of Korea
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Fu T, Pradhan A, He J, He C, Jiang N. Comparison of Wrist and Forearm EMG for Multi-day Biometric Authentication. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-4. [PMID: 38082655 DOI: 10.1109/embc40787.2023.10340339] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
Recently, electromyography (EMG) has been established as a promising new biometric trait that provides a unique dual mode security: biometrics and knowledge. For authentication that is used daily and long-term by general consumers, the wrist is a suitable location, which could be easily integrated into the existing form of smartwatches and fitness trackers. However, current EMG-based biometrics still follow the historical path of powered prosthetics research, where EMG signals were usually recorded from forearm positions. Moreover, the robustness of EMG processing algorithms across multiple days is still an open problem that needs to be addressed before for long-term reliable use. This study intends to investigate the difference in authentication performance between wrist and forearm EMG signals, in a within-day and two cross-day analyses. Our open dataset (GRABMyo dataset) was used to examine this difference, which contains forearm and wrist EMG data collected from 43 participants over three different days with long separation (Days 1, 8, and 29). The results showed wrist EMG signals led to at least comparable with forearm EMG signals in within-day Equal-error rate (EER). In cross-day analysis, the EER of the wrist EMG signals was higher than that of forearm signals. In general, the low median EER (<0.1) of wrist EMG in cumulative cross-day analysis demonstrates the promise of using wrist EMG signals for authentication in long-term applications.
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Pradhan A, He J, Jiang N. Cross-day analysis of Multicode Surface Electromyography based Biometrics for Personal Identification. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-4. [PMID: 38082784 DOI: 10.1109/embc40787.2023.10340324] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
Recently, surface electromyography (sEMG) has emerged as a novel biometric trait for personal identification, potentially providing a superior spoof-resistant solution over existing traits. The sEMG possesses a unique dual-mode security: they differ between individuals (biometric-mode), and different gestures have different sEMG characteristics (knowledge-mode). To leverage the knowledge-mode facet of the dual-mode security, the previous studies have utilized a multicode framework involving the fusion of codes (gestures), however, the analysis involved data recorded on a single day and from a small subject-pool. In this study, wrist EMG data collected from 43 participants over three different days while performing static hand/wrist gestures was utilized in two cross-day analyses, where the training and testing data were from different days. Three levels of fusion, score, rank, and decision were investigated to determine the optimal fusion scheme. The results showed that the score-level fusion scheme resulted in a median rank-1 accuracy of 77.9% and rank-5 accuracy of 99.6%, all significantly higher (p<0.001) than the respective single-code gesture. Our results showed that the multicode sEMG biometric framework provides superior identification performance in a more realistic cross-day scenario.
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Sánchez-Reolid R, López de la Rosa F, Sánchez-Reolid D, López MT, Fernández-Caballero A. Machine Learning Techniques for Arousal Classification from Electrodermal Activity: A Systematic Review. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22228886. [PMID: 36433482 PMCID: PMC9695360 DOI: 10.3390/s22228886] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/19/2022] [Revised: 11/14/2022] [Accepted: 11/14/2022] [Indexed: 05/14/2023]
Abstract
This article introduces a systematic review on arousal classification based on electrodermal activity (EDA) and machine learning (ML). From a first set of 284 articles searched for in six scientific databases, fifty-nine were finally selected according to various criteria established. The systematic review has made it possible to analyse all the steps to which the EDA signals are subjected: acquisition, pre-processing, processing and feature extraction. Finally, all ML techniques applied to the features of these signals for arousal classification have been studied. It has been found that support vector machines and artificial neural networks stand out within the supervised learning methods given their high-performance values. In contrast, it has been shown that unsupervised learning is not present in the detection of arousal through EDA. This systematic review concludes that the use of EDA for the detection of arousal is widely spread, with particularly good results in classification with the ML methods found.
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Affiliation(s)
- Roberto Sánchez-Reolid
- Departamento de Sistemas Informáticos, Universidad de Castilla-La Mancha, 02071 Albacete, Spain
- Neurocognition and Emotion Unit, Instituto de Investigación en Informática, 02071 Albacete, Spain
| | | | - Daniel Sánchez-Reolid
- Neurocognition and Emotion Unit, Instituto de Investigación en Informática, 02071 Albacete, Spain
| | - María T. López
- Departamento de Sistemas Informáticos, Universidad de Castilla-La Mancha, 02071 Albacete, Spain
- Neurocognition and Emotion Unit, Instituto de Investigación en Informática, 02071 Albacete, Spain
| | - Antonio Fernández-Caballero
- Departamento de Sistemas Informáticos, Universidad de Castilla-La Mancha, 02071 Albacete, Spain
- Neurocognition and Emotion Unit, Instituto de Investigación en Informática, 02071 Albacete, Spain
- CIBERSAM-ISCIII (Biomedical Research Networking Center in Mental Health, Instituto de Salud Carlos III), 28016 Madrid, Spain
- Correspondence:
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Jang Y, Moon JH, Lee C, Lee SM, Kim H, Song GH, Spinks GM, Wallace GG, Kim SJ. A Coiled Carbon Nanotube Yarn-Integrated Surface Electromyography System To Monitor Isotonic and Isometric Movements. ACS APPLIED MATERIALS & INTERFACES 2022; 14:45149-45155. [PMID: 36169191 DOI: 10.1021/acsami.2c11811] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
A surface electromyogram (sEMG) electrode collects electrical currents generated by neuromuscular activity by a noninvasive technique on the skin. It is particularly attractive for wearable systems for various human activities and health care monitoring. However, it remains challenging to discriminate EMG signals from isotonic (concentric/eccentric) and isometric movements. By applying nanotechnology, we provide a coiled carbon nanotube (CNT) yarn-integrated sEMG device to overcome sEMG-based motion recognition. When the arm was contracted at different angles, the sEMG-derived root mean square amplitude signals were constant regardless of the angle of the moving arm. However, the coiled CNT yarn-derived open circuit voltage (OCV) signals proportionally increased when the arm's angle increased, and presented negative and positive values depending on the moving direction of the arm. Moreover, isometric contraction is characterized by the onset of EMG signals without an OCV signal, and isotonic contraction is determined by both EMG signals and OCV signals. Taken together, the integration of EMG and coiled CNT yarn electrodes provides complementary information, including the strength, direction, and degree of muscle movement. Therefore, we suggest that our system has high potential as a wearable system to monitor human motions in industrial and human system applications.
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Affiliation(s)
- Yongwoo Jang
- Center for Self-Powered Actuation, Department of Biomedical Engineering, Hanyang University, Seoul 04763, South Korea
- Department of Pharmacology, College of Medicine, Hanyang University, Seoul 04736, Korea
| | - Ji Hwan Moon
- Center for Self-Powered Actuation, Department of Electronic Engineering, Hanyang University, Seoul 04763, South Korea
| | - Chanho Lee
- Center for Self-Powered Actuation, Department of Biomedical Engineering, Hanyang University, Seoul 04763, South Korea
| | - Sung Min Lee
- Center for Self-Powered Actuation, Department of Biomedical Engineering, Hanyang University, Seoul 04763, South Korea
| | - Heesoo Kim
- Center for Self-Powered Actuation, Department of Biomedical Engineering, Hanyang University, Seoul 04763, South Korea
| | - Gyu Hyeon Song
- Center for Self-Powered Actuation, Department of Electronic Engineering, Hanyang University, Seoul 04763, South Korea
| | - Geoffrey M Spinks
- Intelligent Polymer Research Institute, ARC Centre of Excellence for Electro Materials Science, AIIM Facility, Innovation Campus, University of Wollongong, North Wollongong, NSW 2522, Australia
| | - Gordon G Wallace
- Intelligent Polymer Research Institute, ARC Centre of Excellence for Electro Materials Science, AIIM Facility, Innovation Campus, University of Wollongong, North Wollongong, NSW 2522, Australia
| | - Seon Jeong Kim
- Center for Self-Powered Actuation, Department of Biomedical Engineering, Hanyang University, Seoul 04763, South Korea
- Center for Self-Powered Actuation, Department of Electronic Engineering, Hanyang University, Seoul 04763, South Korea
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Multi-Session Surface Electromyogram Signal Database for Personal Identification. SUSTAINABILITY 2022. [DOI: 10.3390/su14095739] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Surface electromyogram (sEMG) refers to a biosignal acquired from the skin surface during the contraction of skeletal muscles, and a different signal waveform is generated, depending on the motion performed. Therefore, in contrast to generic personal identification, which uses only a piece of registered information, the sEMG changes the registered information in a personal identification method. The sEMG database (DB) for conventional personal identification has shortcomings, such as a few subjects and the inability to verify sEMG signal variability. In order to solve the problems of DBs, this paper describes a method for constructing a multi-session sEMG DB for many subjects. Data were obtained in two channels when each of the 200 subjects performed 12 motions. There were three sessions, and each motion was repeated 10 times in time intervals of a day or longer between each session. Furthermore, to verify the effectiveness of the constructed sEMG DB, we conducted a personal identification experiment. According to the experimental results, the accuracy for five subjects was 74.19%, demonstrating the applicability of the constructed multi-session sEMG DB.
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Electromyogram-Based Classification of Hand and Finger Gestures Using Artificial Neural Networks. SENSORS 2021; 22:s22010225. [PMID: 35009768 PMCID: PMC8749583 DOI: 10.3390/s22010225] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Revised: 12/21/2021] [Accepted: 12/27/2021] [Indexed: 11/16/2022]
Abstract
Electromyogram (EMG) signals have been increasingly used for hand and finger gesture recognition. However, most studies have focused on the wrist and whole-hand gestures and not on individual finger (IF) gestures, which are considered more challenging. In this study, we develop EMG-based hand/finger gesture classifiers based on fixed electrode placement using machine learning methods. Ten healthy subjects performed ten hand/finger gestures, including seven IF gestures. EMG signals were measured from three channels, and six time-domain (TD) features were extracted from each channel. A total of 18 features was used to build personalized classifiers for ten gestures with an artificial neural network (ANN), a support vector machine (SVM), a random forest (RF), and a logistic regression (LR). The ANN, SVM, RF, and LR achieved mean accuracies of 0.940, 0.876, 0.831, and 0.539, respectively. One-way analyses of variance and F-tests showed that the ANN achieved the highest mean accuracy and the lowest inter-subject variance in the accuracy, respectively, suggesting that it was the least affected by individual variability in EMG signals. Using only TD features, we achieved a higher ratio of gestures to channels than other similar studies, suggesting that the proposed method can improve the system usability and reduce the computational burden.
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Pradhan A, He J, Jiang N. Score, Rank, and Decision-Level Fusion Strategies of Multicode Electromyogram-based Verification and Identification Biometrics. IEEE J Biomed Health Inform 2021; 26:1068-1079. [PMID: 34473636 DOI: 10.1109/jbhi.2021.3109595] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Recent advances in biometric research have established surface electromyogram (sEMG) as a potential spoof-free solution to address some key limitations in current biometric traits. The nature of sEMG signals provide a unique dual-mode security: sEMGs have individual-specific characteristics (biometrics), and users can customize and change gestures just like passcodes. Such security also facilitates the use of code sequences (multicode) to further enhance the security. In this study, three levels of fusion, score, rank, and decision were investigated for two biometric applications, verification and identification. This study involved 24 subjects performing 16 hand/finger gestures, and code sequences with varying codelengths were generated. The performance of the verification and identification system was analyzed for varying codelength (M: 16) and rank (K: 14) to determine the best fusion scheme and desirable parameter values for a multicode sEMG biometric system. The results showed that the decision-level fusion scheme using a weighted majority voting resulted in an average equal error rate of 0.6% for the verification system when M=4. For the identification system, the score-level fusion scheme with score normalization based on fitting a Weibull distribution resulted in a minimum false rejection rate of 0.01% and false acceptance rate of 4.7% using a combination of K=2 and M=4. The results also suggested that the parameters M and K could be adjusted based on the number of users in the database to facilitate optimal performance. In summary, a multicode sEMG biometric system was developed to provide improved dual-mode security based on the personalized codes and biometric traits of individuals, with the combination of enhanced security and flexibility.
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Development of Miniaturized Wearable Wristband Type Surface EMG Measurement System for Biometric Authentication. ELECTRONICS 2021. [DOI: 10.3390/electronics10080923] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
Personal authentication systems employing biometrics are attracting increasing attention owing to their relatively high security compared to existing authentication systems. In this study, a wearable electromyogram (EMG) system that can be worn on the forearm was developed to detect EMG signals and, subsequently, apply them for personal authentication. In previous studies, wet electrodes were attached to the skin for measuring biosignals. Wet electrodes contain adhesives and conductive gels, leading to problems such as skin rash and signal-quality deterioration in long-term measurements. The miniaturized wearable EMG system developed in this study comprised flexible dry electrodes attached to the watch strap, enabling EMG measurements without additional electrodes. In addition, for accurately classifying and applying the measured signal to the personal authentication system, an optimal algorithm for classifying the EMG signals based on a multi-class support vector machine (SVM) model was implemented. The model using cubic SVM achieved the highest personal authentication rate of 87.1%. We confirmed the possibility of implementing a wearable authentication system by measuring the EMG signal and artificial intelligence analysis algorithm presented in this study.
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